of TRMM Microwave Imager (TMI) data into monthly rainfall totals is based on
accumulating histograms of a linear combination of the 19.35 and 21.3 GHz channels.
The rainfall totals are computed by assuming a lognormal distribution of rainrates
and adjusting the parameters of the lognormal distribution to predict the observed
histogram. This procedure fills in the contribution of rainrates too high or too
low to be measured by the TMI. The procedure is also configured to solve for the
brightness temperature in the limit of no rain thereby canceling some calibration
and modeling uncertainties.
The combination of the TRMM Precipitation Radar (PR) with the TMI has permitted
some refinements of the physics assumptions of the retrieval model. In particular,
the comparison of freezing levels inferred from the brightband in the PR data and
retrieved from the TMI uncovered the need for updating the water vapor spectroscopic
assumptions in the model.
A number of advantages would accrue from accumulating histograms of rain rate rather
than brightness temperature. The most notable of these advantages is the ability to
use all of the frequencies of the TMI to expand the dynamic range of the measurements
and reduce the need to fill in high and low rain rates via the lognormal assumption.
Previous attempts as tested in various algorithm intercomparisons have not worked
well. However, the experience developed in these intercomparisons has enabled the design
of an algorithm that avoids the problems that have been uncovered.
The new algorithm collects histograms of rain rate as derived from several different
channels, each valid over a limited dynamic range. The rain rates are smoothed to the
resolution of the 10.7 GHz channel so that the same set of lognormal parameters should
describe each of them. After correcting for offsets, a single histogram is constructed
for each 5 degree square using the most appropriate rain rate retrieval (i.e. frequencies)
for each pixel.
This new algorithm should, in principle, provide more accurate rainfall totals. The
available rain truth over the oceans has proved grossly inadequate for validating
rainfall retrievals and model based error analyses are necessary to establish the
uncertainties of the rainfall estimates.
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